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Article: Bootstrap standard error estimations of nonlinear transport models based on linearly projected data

TitleBootstrap standard error estimations of nonlinear transport models based on linearly projected data
Authors
Issue Date2019
PublisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21
Citation
Transportmetrica A: Transport Science, 2019, v. 15 n. 2, p. 602-630 How to Cite?
AbstractLinear data projection is a commonly leveraged data scaling method for unbiased traffic data estimation. However, recent studies have shown that model estimations based on linearly projected data would certainly result in biased standard errors. Although methods have been developed to remove such biases for linear regression models, many transport models are nonlinear regression models. This study outlines the practical difficulties of the traditional approach to standard error estimation for generic nonlinear transport models, and proposes a bootstrapping mean value restoration method to accurately estimate the parameter standard errors of all nonlinear transport models based on linearly projected data. Comprehensive simulations with different settings using the most commonly adopted nonlinear functions in modeling traffic flow demonstrate that the proposed method outperforms the conventional method and accurately recovers the true standard errors. A case study of estimating a macroscopic fundamental diagram that illustrates situations necessitating the proposed method is presented.
Persistent Identifierhttp://hdl.handle.net/10722/270078
ISSN
2017 Impact Factor: 2.25
2015 SCImago Journal Rankings: 1.352

 

DC FieldValueLanguage
dc.contributor.authorWong, W-
dc.contributor.authorWong, SC-
dc.contributor.authorLiu, HX-
dc.date.accessioned2019-05-20T05:09:05Z-
dc.date.available2019-05-20T05:09:05Z-
dc.date.issued2019-
dc.identifier.citationTransportmetrica A: Transport Science, 2019, v. 15 n. 2, p. 602-630-
dc.identifier.issn2324-9935-
dc.identifier.urihttp://hdl.handle.net/10722/270078-
dc.description.abstractLinear data projection is a commonly leveraged data scaling method for unbiased traffic data estimation. However, recent studies have shown that model estimations based on linearly projected data would certainly result in biased standard errors. Although methods have been developed to remove such biases for linear regression models, many transport models are nonlinear regression models. This study outlines the practical difficulties of the traditional approach to standard error estimation for generic nonlinear transport models, and proposes a bootstrapping mean value restoration method to accurately estimate the parameter standard errors of all nonlinear transport models based on linearly projected data. Comprehensive simulations with different settings using the most commonly adopted nonlinear functions in modeling traffic flow demonstrate that the proposed method outperforms the conventional method and accurately recovers the true standard errors. A case study of estimating a macroscopic fundamental diagram that illustrates situations necessitating the proposed method is presented.-
dc.languageeng-
dc.publisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21-
dc.relation.ispartofTransportmetrica A: Transport Science-
dc.titleBootstrap standard error estimations of nonlinear transport models based on linearly projected data-
dc.typeArticle-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/23249935.2018.1519647-
dc.identifier.hkuros297698-
dc.identifier.volume15-
dc.identifier.spage602-
dc.identifier.epage630-
dc.publisher.placeUnited Kingdom-

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